There has been growing interest for more than a decade in Business Analytics as a means for improving business performance. One of the most popular Business Analytics technique involves monitoring performance by means of Key Performance Indicators (KPIs). A KPI is a powerful tool that relates enterprise data to business goals, thereby enabling managers to guide the analytic process and identify deviations in their strategic plan. Nevertheless, monitoring KPIs requires that they are evaluated at multiple levels of detail, in order to identify potential problems earlier instead of being noted after the fact. Unfortunately, there are obstacles to the generation and enactment of such monitoring processes. In particular, there is no systematic, tool-supported process for defining what is to be monitored given a strategic plan, nor are there tools for automatically generating monitoring queries. As a result, monitoring consists of a manual process whereby queries are generated for high level indicators across a few scorecards and dashboards. In this paper we present a systematic semi-automatic approach that covers the entire monitoring process. Our approach performs a partial search guided by the KPIs of the company, generating queries required during the monitoring process. Thanks to our approach, users become aware of the existence of problems and where they are located, without requiring a priori information about the nature of the problem being searched. Moreover, we have implemented our approach in our CASE tool HERMES and evaluated the results on a case study using real data.

A systematic approach for dynamic targeted monitoring of KPIs

Zoumpatianos, Konstantinos;Palpanas, Themistoklis;Mylopoulos, Ioannis;
2014

Abstract

There has been growing interest for more than a decade in Business Analytics as a means for improving business performance. One of the most popular Business Analytics technique involves monitoring performance by means of Key Performance Indicators (KPIs). A KPI is a powerful tool that relates enterprise data to business goals, thereby enabling managers to guide the analytic process and identify deviations in their strategic plan. Nevertheless, monitoring KPIs requires that they are evaluated at multiple levels of detail, in order to identify potential problems earlier instead of being noted after the fact. Unfortunately, there are obstacles to the generation and enactment of such monitoring processes. In particular, there is no systematic, tool-supported process for defining what is to be monitored given a strategic plan, nor are there tools for automatically generating monitoring queries. As a result, monitoring consists of a manual process whereby queries are generated for high level indicators across a few scorecards and dashboards. In this paper we present a systematic semi-automatic approach that covers the entire monitoring process. Our approach performs a partial search guided by the KPIs of the company, generating queries required during the monitoring process. Thanks to our approach, users become aware of the existence of problems and where they are located, without requiring a priori information about the nature of the problem being searched. Moreover, we have implemented our approach in our CASE tool HERMES and evaluated the results on a case study using real data.
Proceedings of 24th Annual International Conference on Computer Science and Software Engineering
AA. VV.
Riverton
IBM Corporation
A., Maté; Zoumpatianos, Konstantinos; Palpanas, Themistoklis; J., Trujillo; Mylopoulos, Ioannis; E., Koci
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/101718
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